There is a fair amount of evidence that mesh (static multihop wireless)
networks are gaining popularity, both in the academic literature and in the
commercial space. Nonetheless, none of the prior work has evaluated the
feasibility of applications on mesh through the use of deployed networks and real
user traffic. The state of the art is the use of deployed testbeds with synthetic
traces consisting of random traffic patterns.In this paper, we evaluate the
feasibility of a mesh network for an all-wireless office using traces of office
users and an actual 21-node multi-radio mesh testbed in an office area. Unlike
previous mesh studies that have examined routing design in detail, we examine how
different office mesh design choices impact the performance of user traffic. From
our traces of 11 users spanning over a month, we identify 3 one hour trace
periods with different characteristics and evaluate network performance for them.
In addition, we consider different user-server placement, different wireless
hardware, different wireless settings and different routing metrics.We find that
our captured traffic is significantly different from the synthetic workloads
typically used in the prior work. Our trace capture and replay methodology allows
us to directly quantify the feasibility of office meshes by measuring the
additional delay experienced by individual transactions made by user
applications. Performance on our mesh network depends on the routing metric
chosen, the user-server placement and the traffic load period. The choice of
wireless hardware and wireless settings has a significant impact on performance
under heavy load and challenging placement. Ultimately we conclude that for our
traces and deployed system, under most conditions, all-wireless office meshes are
feasible. In most cases, individual transactions incur under 20ms of additional
delay over the mesh network. We believe this is an acceptable delay for most
applications where a wired network to every machine is not readily available. We
argue that our results are scalable to a network of over 100 users.